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首页> 外文会议>The 2011 International Joint Conference on Neural Networks >A neural circuit model for nCRF's dynamic adjustment and its application on image representation
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A neural circuit model for nCRF's dynamic adjustment and its application on image representation

机译：nCRF动态调整的神经电路模型及其在图像表示中的应用

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According to Biology there is a large disinhibitory area outside the classical receptive field (CRF), which is called as non-classical receptive field (nCRF). Combining CRF with nCRF could increase the sparseness, reliability and precision of the neuronal responses. This paper is aimed at the realization of the neural circuit and the dynamic adjustment mechanism of the receptive field (RF) with respect to nCRF. On the basis of anatomical and electrophysiological evidence, we constructed a neural computational model, which can represent natural images faithfully, simply and rapidly. And the representation can significantly improve the subsequent operation efficiency such as segmentation or integration. This study is of particular significance in the development of efficient image processing algorithms based on neurobiological mechanisms. The RF mechanism of ganglion cell (GC) is the result of a long term of evolution and optimization of self-adaptability and high representation efficiency. So its performance evaluation in natural image processing is worthy of further study.
机译：根据生物学，在经典感受野（CRF）之外有一个很大的去抑制区域，被称为非经典感受野（nCRF）。 CRF与nCRF结合使用可增加神经元反应的稀疏性，可靠性和精确度。本文旨在实现神经回路以及相对于nCRF的感受野（RF）的动态调节机制。在解剖学和电生理学证据的基础上，我们构建了一个神经计算模型，该模型可以忠实，简单，快速地表示自然图像。并且该表示可以显着提高后续操作效率，例如分段或集成。这项研究对于开发基于神经生物学机制的有效图像处理算法特别重要。神经节细胞（GC）的RF机制是长期进化和优化自适应性和高表示效率的结果。因此其在自然图像处理中的性能评价值得进一步研究。

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